Gastos Casa
31 octubre, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 10/9/2022 | Comida | 41870 | Andrés | NA |
| 12/9/2022 | Comida | 3020 | Andrés | Reste el costa rama |
| 12/9/2022 | Comida | 20563 | Andrés | Laflordeloto.cl |
| 12/9/2022 | Enceres | 57000 | Andrés | Arreglo wc+ visita |
| 15/9/2022 | Comida | 38863 | Tami | NA |
| 16/9/2022 | Diosi | 12136 | Tami | Pipeta antipulgas |
| 19/9/2022 | Comida | 5070 | Andrés | NA |
| 20/9/2022 | Comida | 62208 | Tami | NA |
| 21/9/2022 | VTR | 17990 | Andrés | Entel con mercadopago |
| 28/9/2022 | Comida | 79070 | Tami | NA |
| 30/9/2022 | Netflix | 8402 | Tami | NA |
| 30/9/2022 | Comida | 28000 | Andrés | Caramagnola |
| 2/10/2022 | Enceres | 10000 | Andrés | Manguera |
| 2/10/2022 | Electricidad | 48087 | Andrés | atrasado del mes anterior |
| 4/10/2022 | Comida | 41760 | Andrés | NA |
| 4/10/2022 | Comida | 12860 | Andrés | NA |
| 8/10/2022 | Brussels | 25300 | Tami | NA |
| 8/10/2022 | Comida | 25300 | Tami | NA |
| 10/10/2022 | Comida | 67895 | Tami | NA |
| 11/10/2022 | Enceres | 11730 | Andrés | Ida easy |
| 11/10/2022 | Enceres | 7146 | Andrés | Uber easy |
| 15/10/2022 | Tres toques | 28600 | Tami | NA |
| 17/10/2022 | Comida | 47140 | Andrés | NA |
| 19/10/2022 | Comida | 28110 | Andrés | FREST verduras y frutas |
| 23/10/2022 | Comida | 76701 | Tami | NA |
| 26/10/2022 | Comida | 35941 | Tami | NA |
| 26/10/2022 | Enceres | 11980 | Andrés | Mascarilla |
| 27/10/2022 | Comida | 17536 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.2941e+08 2 4.8241 0.0084 **
## lag_depvar 7.6928e+10 1 1728.4345 <2e-16 ***
## Residuals 2.2476e+10 505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 968.0196 13489.66 0.0188127
## 2-0 27388.274 21623.9694 33152.58 0.0000000
## 2-1 20159.435 16677.9604 23640.91 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 353 49622.54 15808.211
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2058.573861 4057.378525 -554.918337 2423.467188 -3002.944669
## 7 8 9 10 11
## 506.637723 -5673.651176 -1167.597226 -3943.818156 -374.428343
## 12 13 14 15 16
## -4902.376050 -1545.939210 -836.674132 435.275228 -3198.584978
## 17 18 19 20 21
## -320.175020 -2080.610083 6658.649547 -1531.396077 -1203.200865
## 22 23 24 25 26
## 1484.847938 -1192.484008 234.147062 1689.302349 -7122.467232
## 27 28 29 30 31
## 975.749109 8206.923317 370.277423 -62.286596 -2446.189174
## 32 33 34 35 36
## 1549.587816 4534.747634 1058.537418 2320.350901 -1950.284334
## 37 38 39 40 41
## 4545.572627 4266.980664 -2337.841762 -3020.124841 -1123.429229
## 42 43 44 45 46
## -10745.601110 7360.378751 2568.130689 1358.198902 8087.774248
## 47 48 49 50 51
## 614.112190 6460.741117 6609.125964 -6021.445284 -4876.217001
## 52 53 54 55 56
## -5097.119085 -7926.200889 6186.935115 -4069.776971 -4860.202785
## 57 58 59 60 61
## 3919.477887 916.238278 -13.807342 158.135589 -4983.831897
## 62 63 64 65 66
## 18172.361971 3553.674371 -3747.796147 5861.718767 7246.861437
## 67 68 69 70 71
## 14502.533959 1471.748451 -13418.187069 -1393.549437 4576.089228
## 72 73 74 75 76
## -4991.818863 -4450.462911 -10506.952100 2531.931441 -5360.221767
## 77 78 79 80 81
## 1136.016556 -6810.715366 643.427984 -2274.224580 -2607.303570
## 82 83 84 85 86
## -3837.499944 -427.625655 2414.242632 3833.264122 511.872655
## 87 88 89 90 91
## -457.673608 223.159532 4323.429264 -1174.737906 1148.455347
## 92 93 94 95 96
## -2074.940037 -1039.249998 189.028124 283.245502 -7478.865515
## 97 98 99 100 101
## 2448.625458 -8569.817015 -2851.651436 -3941.111358 -1622.369944
## 102 103 104 105 106
## -1149.195369 3287.776035 -2271.268240 2672.144595 -1108.632090
## 107 108 109 110 111
## 1021.861328 2624.923662 -3139.808689 -4688.368298 -786.930784
## 112 113 114 115 116
## 1964.638624 11733.322230 -1290.400636 2635.234476 4214.735914
## 117 118 119 120 121
## 3430.440951 -1187.902936 -4785.645571 -3751.664856 2321.705118
## 122 123 124 125 126
## -1747.469399 1339.476228 8847.791333 775.448238 61.677801
## 127 128 129 130 131
## -2582.476812 2619.116724 7002.226575 918.675350 -8588.672087
## 132 133 134 135 136
## 1730.755082 4106.852993 -3218.328933 -1445.225569 -866.497443
## 137 138 139 140 141
## -3885.166048 1205.619393 -484.300882 -2900.582188 1749.814360
## 142 143 144 145 146
## -1865.755416 -7802.918541 2117.371526 -3426.245830 2173.158556
## 147 148 149 150 151
## -210.601718 1065.484682 -329.719104 1380.260077 1201.326391
## 152 153 154 155 156
## 3360.755452 -4882.069179 -1158.249936 -3213.643971 5998.592881
## 157 158 159 160 161
## 9741.010181 -3060.950244 -4393.715842 4010.132607 560.498840
## 162 163 164 165 166
## 3050.492777 -5584.129478 -6384.317583 4557.984116 17749.160893
## 167 168 169 170 171
## 3843.585779 -202.381647 -2236.993610 -868.700852 3839.090037
## 172 173 174 175 176
## -3.120484 -7842.524807 3161.647938 4598.932548 866.590374
## 177 178 179 180 181
## 8990.669855 -9069.774581 -3212.314835 -10457.790187 -10875.110798
## 182 183 184 185 186
## 1671.951605 9700.211966 -1110.047486 6252.749955 6826.882276
## 187 188 189 190 191
## 13377.671568 8552.387193 -3992.287662 2586.919066 10485.447325
## 192 193 194 195 196
## -1592.980823 -2358.594254 -10157.019945 -6145.218517 1504.745605
## 197 198 199 200 201
## -4974.258437 -9496.878972 5754.046635 -2753.297806 -1380.375939
## 202 203 204 205 206
## -467.759197 6827.203634 10152.372633 765.195234 3113.100016
## 207 208 209 210 211
## 3269.372687 5939.660298 12953.511438 -5654.775447 -11192.007148
## 212 213 214 215 216
## -5454.225620 -10324.898018 -4728.901992 1902.972360 -12660.736356
## 217 218 219 220 221
## 16832.107016 8097.669187 1748.405879 26899.144228 12524.039136
## 222 223 224 225 226
## 7259.825427 13929.933196 -4083.281738 -1829.053454 3743.872580
## 227 228 229 230 231
## 330.560352 2748.599238 9016.651348 5801.947162 -1943.530580
## 232 233 234 235 236
## -1812.561989 9486.173180 -11499.069675 -7151.805934 -8336.353108
## 237 238 239 240 241
## -9822.469139 3434.306630 1671.818109 -7995.966474 -8627.325113
## 242 243 244 245 246
## 9516.250423 -7437.455420 2868.791660 -9955.359038 -3636.319169
## 247 248 249 250 251
## 1852.868267 1399.752962 -11945.339931 4098.896558 2463.020184
## 252 253 254 255 256
## 4576.552910 2450.288502 -871.491428 11431.865134 21074.678671
## 257 258 259 260 261
## 3220.240106 -4250.702028 4190.553993 -1629.745378 3836.323390
## 262 263 264 265 266
## -4767.558798 -10750.898393 -4483.892324 -238.494672 -4906.358918
## 267 268 269 270 271
## 9097.579545 -4046.064574 4458.708192 -1878.514026 4676.577274
## 272 273 274 275 276
## 914.242815 7504.049563 -1271.571714 12186.726310 -4521.119083
## 277 278 279 280 281
## 1845.049190 -256.587084 7982.074909 -4985.425186 -2597.190046
## 282 283 284 285 286
## -11092.182635 -2392.872175 18949.863972 7904.714490 2797.333309
## 287 288 289 290 291
## -571.671896 987.503049 6487.729306 6929.624411 -18766.856089
## 292 293 294 295 296
## -10935.740345 -7811.158561 10042.772495 3346.520635 -936.562767
## 297 298 299 300 301
## 27655.003641 10063.225289 4832.638976 9439.425455 2726.446761
## 302 303 304 305 306
## -1146.135076 7833.535799 -24396.141137 -3366.081110 37.343177
## 307 308 309 310 311
## -6748.337430 -3681.251087 3257.386905 -8901.667044 -2854.732987
## 312 313 314 315 316
## -7791.761154 2024.257649 -2728.936280 2482.536748 -3688.113736
## 317 318 319 320 321
## 27862.664663 -609.637235 3425.741800 10944.351804 5612.488471
## 322 323 324 325 326
## 32374.992541 4823.995034 -21208.159028 1786.782573 1118.515842
## 327 328 329 330 331
## -6438.270086 -1617.249934 -33116.653765 1406.538773 -1808.596188
## 332 333 334 335 336
## 403.929683 -2689.901437 4576.469637 -7.299457 -6532.151028
## 337 338 339 340 341
## -2639.414818 -1702.287881 -7188.581127 4398.749291 -890.802914
## 342 343 344 345 346
## -1263.880926 -521.912339 639.411094 924.310671 -1197.155867
## 347 348 349 350 351
## -9023.317032 -12706.577452 2922.516547 -3770.572122 -3091.368153
## 352 353 354 355 356
## -5406.573897 2353.004147 1932.812912 3256.928028 -3317.056177
## 357 358 359 360 361
## -48.234487 1128.964887 7438.382558 611.727479 287.403678
## 362 363 364 365 366
## 2903.769730 -2460.804885 -559.087474 -8418.427563 -4211.257085
## 367 368 369 370 371
## -5761.069709 -4448.992981 -6721.999531 5595.963242 865.239949
## 372 373 374 375 376
## 7586.200514 -7266.143255 -1820.322726 -2937.305335 -1997.258613
## 377 378 379 380 381
## -11980.420697 2491.847878 -10098.785705 6316.685306 9857.591919
## 382 383 384 385 386
## 3519.831395 -2051.951903 1969.091513 7081.347466 11667.833899
## 387 388 389 390 391
## -5668.260040 -5155.445388 111.966164 8833.387082 1991.318434
## 392 393 394 395 396
## 11387.140796 -9825.367630 2953.681303 870.134367 722.843142
## 397 398 399 400 401
## -488.849038 -380.822400 -14290.328492 8890.269153 -911.416604
## 402 403 404 405 406
## -1086.912408 7283.498957 -7708.440304 -977.153788 -2198.119865
## 407 408 409 410 411
## -5459.379125 -2439.050300 -3476.411452 -8284.949385 6682.856265
## 412 413 414 415 416
## 2094.221761 -6955.592644 -7206.205891 14771.346559 4176.723745
## 417 418 419 420 421
## 4797.911729 -7785.375825 -4400.527311 -2208.785529 3232.030711
## 422 423 424 425 426
## -13642.018584 -2274.217768 -8573.585902 3614.815731 7511.479679
## 427 428 429 430 431
## 7004.179140 -3648.506554 -3742.595708 -4306.747931 -1334.190707
## 432 433 434 435 436
## -5253.586029 -6122.627114 -5395.113180 -801.710141 -273.854530
## 437 438 439 440 441
## -4423.004614 3159.800176 5356.891532 -4618.453828 -1680.187522
## 442 443 444 445 446
## 2057.992381 -3393.607645 3305.168304 -6159.040076 -11632.280168
## 447 448 449 450 451
## -3920.306798 10253.251283 -1563.374223 5227.183490 -5467.063039
## 452 453 454 455 456
## -666.351751 836.203389 3458.690560 -11882.525474 3879.475942
## 457 458 459 460 461
## -6249.873642 7030.225253 3427.459893 2877.925876 -3508.594486
## 462 463 464 465 466
## 2468.985211 339.299642 2136.077243 -200.745856 3675.863048
## 467 468 469 470 471
## -2351.139587 6125.544268 -6683.976647 -2622.414276 -1831.887975
## 472 473 474 475 476
## -4270.613894 3433.329835 8189.717316 -5716.180674 1853.436438
## 477 478 479 480 481
## -5829.790248 -2430.664043 2447.066409 -12528.195365 -9224.385731
## 482 483 484 485 486
## -591.964871 610.180128 -403.384873 -800.132164 -9054.089669
## 487 488 489 490 491
## 11702.997838 6692.295239 7795.552536 -5146.273750 5719.441152
## 492 493 494 495 496
## 9584.878599 6250.502499 -13326.568902 -10254.110684 -3013.991640
## 497 498 499 500 501
## -652.412127 -72.608686 -7181.969595 1123.801457 4773.905675
## 502 503 504 505 506
## 5932.246863 1015.439621 426.391369 -6895.097824 989.759446
## 507 508 509 510
## -4643.877871 2282.685600 -878.575929 -7735.608688
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17210.71 20081.62 24371.06 24086.68 26459.66 23770.08 24492.37 19684.74
## 10 11 12 13 14 15 16 17
## 19419.10 16739.71 17523.66 14225.80 14277.39 14947.58 16658.30 14964.32
## 18 19 20 21 22 23 24 25
## 16007.61 15375.92 22517.40 21593.77 21069.29 22975.06 22295.42 22953.41
## 26 27 28 29 30 31 32 33
## 24814.75 18692.54 20433.08 28335.72 28393.86 28064.05 25673.70 27087.82
## 34 35 36 37 38 39 40 41
## 30962.89 31314.22 32735.14 30225.00 34176.02 37410.84 34442.41 31226.71
## 42 43 44 45 46 47 48 49
## 30064.89 20565.91 28147.30 30604.09 31702.37 38597.46 38087.83 42788.87
## 50 51 52 53 54 55 56 57
## 47060.45 39697.50 34220.69 29201.92 22289.21 28631.63 25183.77 21450.52
## 58 59 60 61 62 63 64 65
## 25895.62 27165.66 27465.15 27880.40 23716.92 40446.47 42305.80 37512.14
## 66 67 68 69 70 71 72 73
## 41754.14 46710.75 57467.82 55465.04 40585.26 38070.34 41113.39 35366.03
## 74 75 76 77 78 79 80 81
## 30780.38 21406.35 24634.51 20526.27 22629.72 17482.71 19514.94 18735.02
## 82 83 84 85 86 87 88 89
## 17754.64 15807.48 17095.90 20734.02 25188.56 26186.67 26211.84 26833.71
## 90 91 92 93 94 95 96 97
## 30993.17 29813.97 30821.65 28869.96 28063.11 28434.33 28844.29 22368.23
## 98 99 100 101 102 103 104 105
## 25408.39 18380.79 17227.40 15251.80 15554.05 16237.08 20746.98 19822.86
## 106 107 108 109 110 111 112 113
## 23363.20 23151.42 24841.50 27742.24 25219.51 21633.36 21911.08 24579.39
## 114 115 116 117 118 119 120 121
## 35534.40 33712.19 35564.98 38588.27 40560.47 38229.65 33007.52 29318.44
## 122 123 124 125 126 127 128 129
## 31418.61 29684.24 30875.64 38538.69 38178.18 37231.91 34069.31 35865.34
## 130 131 132 133 134 135 136 137
## 41308.18 40743.81 31872.24 33147.58 36363.90 32744.65 31118.50 30195.88
## 138 139 140 141 142 143 144 145
## 26724.24 28150.44 27918.15 25585.19 27626.47 26239.78 19788.63 22844.39
## 146 147 148 149 150 151 152 153
## 20652.98 23654.89 24199.37 25803.00 25986.60 27654.53 28966.10 32023.50
## 154 155 156 157 158 159 160 161
## 27455.96 26712.79 24247.69 30190.85 41081.38 39397.72 36740.72 41802.79
## 162 163 164 165 166 167 168 169
## 43223.08 46667.42 42095.60 37363.73 42834.12 59271.99 61502.52 59903.42
## 170 171 172 173 174 175 176 177
## 56702.70 55088.62 57813.69 56829.67 49057.64 51904.64 55678.41 55714.90
## 178 179 180 181 182 183 184 185
## 62903.06 53326.31 50050.22 40782.40 32251.33 35788.79 45976.33 45427.82
## 186 187 188 189 190 191 192 193
## 51430.12 57222.90 68095.61 73422.43 67064.65 67259.70 74388.84 70029.31
## 194 195 196 197 198 199 200 201
## 65514.88 54669.22 48649.68 50085.83 45643.88 37747.52 44225.73 42438.38
## 202 203 204 205 206 207 208 209
## 42073.33 42555.65 49406.20 58369.38 57995.90 59735.06 61404.63 65227.35
## 210 211 212 213 214 215 216 217
## 74772.63 66789.58 54880.37 49444.33 40365.76 37298.17 40437.74 30374.89
## 218 219 220 221 222 223 224 225
## 47489.62 54871.31 55780.71 78735.53 86292.89 88312.78 95967.28 86842.91
## 226 227 228 229 230 231 232 233
## 80791.41 80369.87 76991.97 76146.49 80922.91 82298.53 76687.70 71860.83
## 234 235 236 237 238 239 240 241
## 77561.50 64098.23 56068.50 47952.18 39493.98 43720.75 45891.40 39287.61
## 242 243 244 245 246 247 248 249
## 32914.61 43282.60 37481.64 41450.07 33649.60 32344.70 36030.39 38877.77
## 250 251 252 253 254 255 256 257
## 29630.96 35618.41 39451.45 44689.43 47430.35 46918.71 57305.32 74948.05
## 258 259 260 261 262 263 264 265
## 74761.56 68016.59 69510.75 65700.11 67158.27 60864.04 50049.46 46043.78
## 266 267 268 269 270 271 272 273
## 46254.93 42329.28 51206.64 47448.72 51629.94 49730.85 53832.04 54130.52
## 274 275 276 277 278 279 280 281
## 60198.00 57812.56 67565.98 61440.24 61652.02 59987.35 65778.00 59456.33
## 282 283 284 285 286 287 288 289
## 55991.61 45457.02 43840.42 61216.00 66792.10 67204.96 64601.07 63680.84
## 290 291 292 293 294 295 296 297
## 67715.09 71657.86 52496.31 42516.02 36477.23 46884.48 50153.28 49259.85
## 298 299 300 301 302 303 304 305
## 73657.49 79652.36 80325.57 84976.41 83159.99 78148.89 81644.57 56334.51
## 306 307 308 309 310 311 312 313
## 52564.51 52241.62 45980.11 43166.33 46799.67 39289.88 38001.33 32517.60
## 314 315 316 317 318 319 320 321
## 36333.65 35508.18 39371.54 37339.19 63340.21 61163.40 62800.51 70865.23
## 322 323 324 325 326 327 328 329
## 73272.44 98966.29 97330.44 72959.36 71747.20 70090.84 61975.54 59073.80
## 330 331 332 333 334 335 336 337
## 28771.89 32490.17 32933.36 35272.62 34607.96 40423.01 41507.58 36715.56
## 338 339 340 341 342 343 344 345
## 35923.43 36051.15 31331.11 37380.09 38049.02 38309.63 39192.73 40993.55
## 346 347 348 349 350 351 352 353
## 42830.73 42580.32 35466.15 25955.34 31344.57 30196.08 29782.72 27379.28
## 354 355 356 357 358 359 360 361
## 32097.19 35882.79 40383.63 38557.52 39828.32 41984.62 49441.56 49996.74
## 362 363 364 365 366 367 368 369
## 50200.09 52683.80 50146.23 49586.14 42169.97 39343.36 35488.42 33248.57
## 370 371 372 373 374 375 376 377
## 29273.47 36622.19 38928.23 46879.57 40800.89 40243.45 38768.54 38297.42
## 378 379 380 381 382 383 384 385
## 29088.87 33725.36 26719.03 35006.98 45426.31 49021.52 47280.48 49288.80
## 386 387 388 389 390 391 392 393
## 55560.88 65125.55 58280.16 52702.18 52428.61 59869.82 60397.57 69138.65
## 394 395 396 397 398 399 400 401
## 58153.32 59733.29 59289.73 58769.28 57243.54 55994.76 42642.73 51300.13
## 402 403 404 405 406 407 408 409
## 50292.20 49249.79 55704.58 48184.73 47490.12 45802.81 41443.91 40264.84
## 410 411 412 413 414 415 416 417
## 38312.52 32357.29 40295.92 43246.74 37874.49 32921.65 47917.70 51794.66
## 418 419 420 421 422 423 424 425
## 55756.80 48162.96 44455.50 43120.40 46736.88 35059.07 34786.01 28996.76
## 426 427 428 429 430 431 432 433
## 34633.38 43030.68 49980.51 46718.88 43763.03 40662.48 40549.73 36998.06
## 434 435 436 437 438 439 440 441
## 33104.11 30315.00 31904.28 33769.15 31757.06 36663.97 42921.45 39646.62
## 442 443 444 445 446 447 448 449
## 39350.15 42381.75 40250.12 44273.04 39480.14 30437.31 29265.03 40717.09
## 450 451 452 453 454 455 456 457
## 40395.96 46094.49 41694.07 42046.65 43680.74 47430.10 37219.52 42109.45
## 458 459 460 461 462 463 464 465
## 37494.35 45126.83 48676.36 51318.88 48021.01 50381.41 50584.64 52346.32
## 466 467 468 469 470 471 472 473
## 51839.71 54808.14 52114.03 57207.55 50410.99 48001.89 46576.19 43172.24
## 474 475 476 477 478 479 480 481
## 46959.85 54485.75 48865.99 50583.50 45328.66 43694.08 46550.77 35876.24
## 482 483 484 485 486 487 488 489
## 29383.82 31268.82 33988.10 35490.56 36464.52 30052.00 42687.28 49403.30
## 490 491 492 493 494 495 496 497
## 56290.85 50957.99 55831.55 63529.21 67372.57 53513.68 44012.56 42020.98
## 498 499 500 501 502 503 504 505
## 42346.89 43144.68 37585.20 40004.24 45350.18 51079.42 51795.04 51906.53
## 506 507 508 509 510
## 45555.67 46906.88 43134.74 45913.29 45576.18
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8516
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.824088 0.5797503 2.968039
## t2* 1728.434492 27.5895106 247.541011
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.379412 4.942401 10.92683
## 2 lag_depvar 1377.568606 1742.600660 2185.86653Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Oct 31 01:03:07 2022
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# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 6.092444 | 5.953429 | 7.402273 |
| Comida | NA | 301.994333 | 311.081333 | 340.845818 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.415778 | 35.477048 | 30.068879 |
| Enceres | NA | 20.355000 | 17.181381 | 25.119788 |
| Farmacia | NA | 2.442222 | 9.044429 | 10.859818 |
| Gas/Bencina | NA | 50.616667 | 29.454952 | 25.019818 |
| Diosi | NA | 16.129222 | 37.019857 | 37.056758 |
| donaciones/regalos | NA | 0.000000 | 8.194381 | 8.324818 |
| Electrodomésticos/ Mantención casa | NA | 5.258667 | 34.593714 | 25.135394 |
| VTR | NA | 26.212222 | 22.140619 | 21.039939 |
| Netflix | NA | 7.695444 | 7.314476 | 7.653667 |
| Otros | NA | 4.201444 | 1.800619 | 1.145849 |
| Total | 0 | 484.413444 | 519.256238 | 539.672818 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1774, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-11-09 00:04:58 sería de: 35.861 pesos// Percentil 95% más alto proyectado: 39.213,26
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34796.97 | 34765.35 |
| Lo.80 | 34898.42 | 34872.29 |
| Point.Forecast | 35861.45 | 37679.41 |
| Hi.80 | 37695.06 | 42292.55 |
| Hi.95 | 38703.37 | 44734.59 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3074 989.6778
## s.e. 0.1493 35.6567
##
## sigma^2 = 28420: log likelihood = -287.07
## AIC=580.13 AICc=580.73 BIC=585.49paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,1) errors
##
## Coefficients:
## ar1 ma1 xreg
## 0.8430 -0.5655 32.3225
## s.e. 0.1691 0.2523 2.3186
##
## sigma^2 = 28738: log likelihood = -286.9
## AIC=581.79 AICc=582.82 BIC=588.93forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 859.6077 | 642.4514 | 752.2061 |
| Lo.80 | 988.6958 | 762.6386 | 835.2950 |
| Point.Forecast | 1232.5490 | 989.6776 | 1018.1159 |
| Hi.80 | 1476.4023 | 1216.7166 | 1240.9507 |
| Hi.95 | 1605.4904 | 1336.9038 | 1378.0265 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.1
## [7] ggiraph_0.8.3 tidytext_0.3.4 DT_0.26
## [10] autoplotly_0.1.4 rvest_1.0.3 plotly_4.10.0
## [13] xts_0.12.2 forecast_8.18 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-9
## [19] NLP_0.2-1 tsibble_1.1.3 forcats_0.5.2
## [22] dplyr_1.0.10 purrr_0.3.5 tidyr_1.2.1
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.8 httr_1.4.4
## [34] readxl_1.4.1 zoo_1.8-11 stringr_1.4.1
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.4
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.3
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.2.0 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.8.1 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.40 uuid_1.1-0 rstudioapi_0.14
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.2
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.6.3 fBasics_4021.93 rprojroot_2.0.3
## [25] vctrs_0.4.2 generics_0.1.3 xfun_0.33
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.6.0
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.1
## [37] spatial_7.3-14 timeDate_4021.106 rlang_1.0.6
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.1 selectr_0.4-2
## [46] broom_1.0.1 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.9 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.3 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
## [58] Rcpp_1.0.9 base64enc_0.1-3 fracdiff_1.5-1
## [61] haven_2.5.1 fs_1.5.2 magrittr_2.0.3
## [64] timeSeries_4021.105 lmtest_0.9-40 reprex_2.0.2
## [67] googledrive_2.0.0 mvtnorm_1.1-3 sjmisc_2.8.9
## [70] hms_1.1.2 evaluate_0.17 xtable_1.8-4
## [73] sjstats_0.18.1 ggeffects_1.1.4 compiler_4.1.2
## [76] KernSmooth_2.23-20 crayon_1.5.2 minqa_1.2.5
## [79] htmltools_0.5.3 tzdb_0.3.0 lubridate_1.8.0
## [82] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [85] boot_1.3-28 Matrix_1.5-1 car_3.1-1
## [88] cli_3.4.1 quadprog_1.5-8 parallel_4.1.2
## [91] insight_0.18.6 igraph_1.3.5 pkgconfig_2.0.3
## [94] xml2_1.3.3 bslib_0.4.0 estimability_1.4.1
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_1.0.0
## [100] digest_0.6.29 parameters_0.19.0 janitor_2.1.0
## [103] rmarkdown_2.17 cellranger_1.1.0 curl_4.3.3
## [106] gtools_3.9.3 urca_1.3-3 nloptr_2.0.3
## [109] lifecycle_1.0.3 nlme_3.1-153 jsonlite_1.8.2
## [112] tseries_0.10-52 carData_3.0-5 viridisLite_0.4.1
## [115] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.13.0 bit_4.0.4
## [121] sass_0.4.2 performance_0.10.0 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))